Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

sulcus

termed as ., which combines POI data and road network data to generate the distribution of traffic flows. Our model has two novel modules: a graph reconstruction module and a POI supervised contrastive module. The graph reconstruction module includes a .-NN graph builder and a .-NN graph aggregator

observatory

https://doi.org/10.1007/978-3-642-59504-2 propose a self-adaptive . solution with a built-in suboperator cost model that dynamically selects the best . strategy at runtime according to the data skew of the target query. We implement the solution in the commercial shared-nothing ., namely CockroachDB and empirical study justifies that the s

財(cái)產(chǎn)

Microaneurysm

interference

胰臟

MIPM: A Multidimensional Information Perception Model for?Estimating Time of?Arrival on?Real Road Neom Raw-ETA. After that, we design a SPETAformer block to ensure that the extract module can capture the multidimensional correlation of the above features. The recurrent module further enhances the learning ability of the MIPM in the temporal domain by week, daily, recent, and weather four different

harangue

TimeGAE: A Multivariate Time-Series Generation Method via?Graph Auto Encoderrelationships. We also incorporate transformer-encoder or RNN modules to enhance the ability to retain temporal dynamics for time series feature extraction. Several comparisons and ablation experiments on three multivariate time series datasets have been conducted. Our results demonstrate that TimeG

改正

Beyond SweepLine: Efficient MaxRS Queries over?Inaccurate Location Datar algorithm to address two more complex scenarios: the dynamic MaxRS (DMaxRS) query, which accounts for the varying locations of objects over time, and the MaxRS query on trajectory data (MaxRST), where each object is represented by a sequence of points, not just a singular point. Our experimental r

Dri727

Flexible Contact Correlation Learning on?Spatio-Temporal Trajectories potential contact positions using a soft selection module. The contact scores are then derived from the embeddings of the contact trajectory parts. Experiments on two real-world datasets show that ST-TCN outperforms baseline solutions and exhibits superior efficiency in terms of both running time a

diabetes